Abstract
Disease diagnosis can provide crucial information for clinical decisions that influence the outcome in acute serious illness, and this is particularly in the intensive care unit (ICU). However, the central role of diagnosis in clinical practice is challenged by evidence that does not always benefit patients and that factors other than disease are important in determining patient outcome. To streamline the diagnostic process in daily routine and avoid misdiagnoses, in this paper, we proposed a deep multi-source multi-task attention model (DMMAM) for ICU disease diagnosis. DMMAM exploits multi-sources information from various types of complications, clinical measurements, and the medical treatments to support the diagnosis. We evaluate the proposed model with 50 diseases of 9 classifications on an extensive collection of real-world ICU Electronic Health Records (EHR) dataset with 151729 ICU admissions from 46520 patients. Experiments results demonstrate the effectiveness and the robustness of our model.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Data available at https://mimic.physionet.org/.
References
Ahmadi, H., Gholamzadeh, M., Shahmoradi, L., Nilashi, M., Rashvand, P.: Diseases diagnosis using fuzzy logic methods: a systematic and meta-analysis review. Comput. Methods Programs Biomed. 161, 145 (2018)
Azar, A.T., El-Metwally, S.M.: Decision tree classifiers for automated medical diagnosis. Neural Comput. Appl. 23(7–8), 2387–2403 (2013)
Blaxter, M.: Diagnosis as category and process: the case of alcoholism. Soc. Sci. Med. Part A Med. Psychol. Med. Sociol. 12, 9–17 (1978)
Chaurasia, V., Pal, S.: A novel approach for breast cancer detection using data mining techniques (2017)
Che, Z., Purushotham, S., Khemani, R., Liu, Y.: Interpretable deep models for ICU outcome prediction. In: AMIA Annual Symposium Proceedings, vol. 2016, p. 371. American Medical Informatics Association (2016)
Chen, M., Hao, Y., Hwang, K., Wang, L., Wang, L.: Disease prediction by machine learning over big data from healthcare communities. IEEE Access 5, 8869–8879 (2017)
Choi, E., Bahadori, M.T., Schuetz, A., Stewart, W.F., Sun, J.: Doctor AI: predicting clinical events via recurrent neural networks. In: Machine Learning for Healthcare Conference, pp. 301–318 (2016)
Del Mar, C., Doust, J., Glasziou, P.: Clinical thinking; evidence, communication and decision-making (2006)
Detemmerman, L., Olivier, S., Bours, V., Boemer, F.: Innovative PCR without dna extraction for African sickle cell disease diagnosis. Hematology 23(3), 181–186 (2018)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)
Hao, Y., Zuo, W., Shi, Z., Yue, L., Xue, S., He, F.: Prognosis of thyroid disease using MS-apriori improved decision tree. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11061, pp. 452–460. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99365-2_40
Johnson, A.E., et al.: Mimic-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016)
Johnson, M.J., Willsky, A.S.: Bayesian nonparametric hidden semi-Markov models. J. Mach. Learn. Res. 14, 673–701 (2013)
Jutel, A., Nettleton, S., et al.: Towards a sociology of diagnosis: reflections and opportunities. Soc. Sci. Med. 73(6), 793–800 (2011)
Lin, C., et al.: Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM. In: 2018 IEEE International Conference on Healthcare Informatics (ICHI), pp. 219–228. IEEE (2018)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp. 2980–2988 (2017)
Long, N.C., Meesad, P., Unger, H.: A highly accurate firefly based algorithm for heart disease prediction. Expert Syst. Appl. 42(21), 8221–8231 (2015)
Marshall, J.C.: Measurements in the intensive care unit: what do they mean? Crit. Care 7(6), 415 (2003)
Nguyen, C., Wang, Y., Nguyen, H.N.: Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic. J. Biomed. Sci. Eng. 6(05), 551 (2013)
Nilashi, M., Ahmadi, H., Shahmoradi, L., Ibrahim, O., Akbari, E.: A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique. J. Infect. Public Health 12, 13 (2018)
Park, I.H., et al.: Disease-specific induced pluripotent stem cells. Cell 134(5), 877–886 (2008)
Polivka, J., Kralickova, M., Kaiser, C., Kuhn, W., Golubnitschaja, O.: Mystery of the brain metastatic disease in breast cancer patients: improved patient stratification, disease prediction and targeted prevention on the horizon? EPMA J. 8(2), 119–127 (2017)
Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)
Shi, Z., Zuo, W., Chen, W., Yue, L., Han, J., Feng, L.: User relation prediction based on matrix factorization and hybrid particle swarm optimization. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 1335–1341. International World Wide Web Conferences Steering Committee (2017)
Sicherer, S.H., Sampson, H.A.: Food allergy: a review and update on epidemiology, pathogenesis, diagnosis, prevention, and management. J. Allergy Clin. Immunol. 141(1), 41–58 (2018)
Song, H., Rajan, D., Thiagarajan, J.J., Spanias, A.: Attend and diagnose: clinical time series analysis using attention models. arXiv preprint arXiv:1711.03905 (2017)
Subasi, A.: Classification of EMG signals using PSO optimized SVM for diagnosis of neuromuscular disorders. Comput. Biol. Med. 43(5), 576–586 (2013)
Tangri, N., et al.: A predictive model for progression of chronic kidney disease to kidney failure. JAMA 305(15), 1553–1559 (2011)
Trask, A., Gilmore, D., Russell, M.: Modeling order in neural word embeddings at scale. arXiv preprint arXiv:1506.02338 (2015)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Zhang, D., Shen, D., Initiative, A.D.N., et al.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59(2), 895–907 (2012)
Acknowledgement
This work was supported by the Nature Science Foundation of Jilin Province (20180101330JC, 20190302029GX), the Fundamental Research Funds for the Central Universities (No. 2412017QD028), the China Postdoctoral Science Foundation (No. 2017M621192), the Scientific and Technological Development Program of Jilin Province (No. 20180520022JH, 20190302109GX). The authors also gratefully acknowledge the financial support from China Scholarship Council (No. 201706170617).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, Z., Zuo, W., Chen, W., Yue, L., Hao, Y., Liang, S. (2019). DMMAM: Deep Multi-source Multi-task Attention Model for Intensive Care Unit Diagnosis. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-18579-4_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18578-7
Online ISBN: 978-3-030-18579-4
eBook Packages: Computer ScienceComputer Science (R0)